New Collaborative Filtering Algorithms Based on SVD++ and Differential Privacy

Zhengzheng Xian, Qiliang Li, Gai Li and Lei Li

Mathematical Problems in Engineering, 2017, vol. 2017, 1-14

Abstract:

Collaborative filtering technology has been widely used in the recommender system, and its implementation is supported by the large amount of real and reliable user data from the big-data era. However, with the increase of the users’ information-security awareness, these data are reduced or the quality of the data becomes worse. Singular Value Decomposition (SVD) is one of the common matrix factorization methods used in collaborative filtering, which introduces the bias information of users and items and is realized by using algebraic feature extraction. The derivative model SVD++ of SVD achieves better predictive accuracy due to the addition of implicit feedback information. Differential privacy is defined very strictly and can be proved, which has become an effective measure to solve the problem of attackers indirectly deducing the personal privacy information by using background knowledge. In this paper, differential privacy is applied to the SVD++ model through three approaches: gradient perturbation, objective-function perturbation, and output perturbation. Through theoretical derivation and experimental verification, the new algorithms proposed can better protect the privacy of the original data on the basis of ensuring the predictive accuracy. In addition, an effective scheme is given that can measure the privacy protection strength and predictive accuracy, and a reasonable range for selection of the differential privacy parameter is provided.

Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2017/1975719.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2017/1975719.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1975719

DOI: 10.1155/2017/1975719

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().